

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
     __lowercase     : str       =						argparse.ArgumentParser(
         description=(
             '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
             ''' Distillation'''
         )
     )
     parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
     parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
     parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
     parser.add_argument('''--vocab_transform''', action='''store_true''')
     __lowercase     : List[str]       =						parser.parse_args()

     if args.model_type == "bert":
          __lowercase     : int       =						BertForMaskedLM.from_pretrained(args.model_name)
          __lowercase     : List[Any]       =						'''bert'''
     else:
          raise ValueError('''args.model_type should be "bert".''')

     __lowercase     : Dict       =						model.state_dict()
     __lowercase     : Optional[Any]       =						{}

     for w in ["word_embeddings", "position_embeddings"]:
          __lowercase     : Union[str, Any]       =						state_dict[f'''{prefix}.embeddings.{w}.weight''']
     for w in ["weight", "bias"]:
          __lowercase     : List[str]       =						state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

     __lowercase     : Tuple       =						0
     for teacher_idx in [0, 2, 4, 7, 9, 11]:
          for w in ["weight", "bias"]:
               __lowercase     : List[str]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
               ]
               __lowercase     : Union[str, Any]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
               ]
               __lowercase     : int       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
               ]

               __lowercase     : Any       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
               ]
               __lowercase     : Dict       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
               ]

               __lowercase     : Tuple       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
               ]
               __lowercase     : Dict       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
               ]
               __lowercase     : Optional[Any]       =						state_dict[
                   f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
               ]
          std_idx += 1

     __lowercase     : Any       =						state_dict['''cls.predictions.decoder.weight''']
     __lowercase     : List[str]       =						state_dict['''cls.predictions.bias''']
     if args.vocab_transform:
          for w in ["weight", "bias"]:
               __lowercase     : Any       =						state_dict[f'''cls.predictions.transform.dense.{w}''']
               __lowercase     : str       =						state_dict[f'''cls.predictions.transform.LayerNorm.{w}''']

     print(f'''N layers selected for distillation: {std_idx}''')
     print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

     print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
     torch.save(compressed_sd, args.dump_checkpoint)







import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3_0     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(image_size // patch_size) ** 2
         snake_case											=							num_patches + 1
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							ViTConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   )

         return config, pixel_values
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							FlaxViTModel(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(self.image_size, self.image_size)
         snake_case											=							(self.patch_size, self.patch_size)
         snake_case											=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.type_sequence_label_size
         snake_case											=							FlaxViTForImageClassification(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         snake_case											=							1
         snake_case											=							FlaxViTForImageClassification(__snake_case       )

         snake_case											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case											=							model(__snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,    (
             snake_case
         )					,    
         )											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def 						a_     (			self       ):
         snake_case											=							FlaxViTModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.__call__       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__       ):
                   snake_case											=							self._prepare_for_class(__snake_case     ,   __snake_case       )
                   snake_case											=							model_class(__snake_case       )

                   @jax.jit
                   def model_jitted(__snake_case     ,   **__snake_case       ):
                        return model(pixel_values=__snake_case     ,   **__snake_case       )

                   with self.subTest('''JIT Enabled'''       ):
                        snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                        with jax.disable_jit():
                             snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   self.assertEqual(len(__snake_case       )     ,   len(__snake_case       )       )
                   for jitted_output, output in zip(__snake_case     ,   __snake_case       ):
                        self.assertEqual(jitted_output.shape     ,   output.shape       )




    @slow
    def 						a_     (			self       ):
         for model_class_name in self.all_model_classes:
              snake_case											=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
              snake_case											=							model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
              self.assertIsNotNone(__snake_case       )




